Leak Detection and Repair (LDAR) is the process by which a facility locates and repairs leaking components, including valves, pumps, flanges and connectors, in order to reduce the emissions of fugitive volatile organic compounds (VOCs), like methane, and hazardous air pollutants (HAPs), such as benzene. Left unmonitored and unaddressed, these leaks can have detrimental consequences for the environment and human health.
The leaks that are detected are called fugitive emissions, so named because they typically elude detection without specialised equipment. Nevertheless, the collective impact of these small leaks across numerous components can be substantial, and so repairing the leaks will result in a significant decrease in harmful gas emissions from the plant.
Detecting Emissions
In conventional LDAR programs, fugitive emissions are detected using organic vapor analyzers (OVAs) in a process known as “sniffing.” The OVA probe is positioned near the leak source to measure the concentration of hydrocarbons in the sampled atmosphere in parts per million (ppm). This enables the facility to identify components with significant leaks and prioritize their repair at the next available opportunity, thus making the most substantial impact on VOC reduction. Subsequently, the facility can establish specific criteria for defining emissions as leaks. For instance, a typical leak definition might set the threshold at 500 ppm, considering any reading above this value as a leak. This systematic approach allows the plant to concentrate its efforts on addressing those components effectively.
Emissions can also be detected using optical gas imaging (OGI) cameras. These cameras work on the principle of infrared (IR) imaging, but with a special filter enabling them to detect infrared energy within the waveband characteristic of hydrocarbons such as methane. This allows the camera to detect leaks too small for traditional infrared cameras. OGI cameras allow real-time visualisation leaks and are excellent for scanning large areas of plant to identify leaks quickly, including those in hard to reach locations. Although they cannot directly measure the size of the leak, there is quantification software available that can estimate the concentration of the emission. The integration of these techniques can enhance the efficiency of LDAR programs by rapidly identifying potential leaks through the camera and then precisely quantifying emissions once a leak is identified.
Quantifying Leaks
The quanitifcation and reporting of emissions are fundamental components of LDAR programs. To convert the measured leak concentrations, in parts per million (ppm), into mass flow rates, typically measured in kilograms per year (kg/year), industries utilise a series of correlations using data published by the EPA. These correlations provide a means to estimate the volume of emissions from leaks, aiding in environmental reporting and regulatory compliance, and allowing for more informed decision-making and targeted emission reduction efforts.
LDAR Applications
The LDAR process is essential to industries that handle VOCs and HAPs. This includes petrochemical, oil and gas, chemical manufacturing, pharmaceutical, waste management, manufacturing, and emerging sectors like biogas production. In recent years, Europe has witnessed the introduction of more stringent regulations governing LDAR practices. As a result, companies and facilities operating within the European Union are required to enhance their LDAR programs, employ advanced monitoring techniques, and implement more robust reporting and record-keeping procedures to ensure compliance with these rigorous new standards.
Conclusion
In conclusion, LDAR offers numerous advantages to industries and the environment. It helps mitigate the release of harmful VOCs and HAPs, safeguarding human health and reducing air pollution. By identifying and addressing fugitive emissions, LDAR promotes compliance with environmental regulations, minimises operational risks, and contributes to a more sustainable and responsible approach to industrial processes.
